Python

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General


Important Libraries

Manual: https://docs.scipy.org/doc/numpy/user/quickstart.html
Reference: https://docs.scipy.org/doc/numpy/reference/
NumPy is the fundamental package for scientific computing with Python. It contains among other things:
-- a powerful N-dimensional array object
-- sophisticated (broadcasting) functions
-- tools for integrating C/C++ and Fortran code
-- useful linear algebra, Fourier transform, and random number capabilities
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.


Manual: http://pandas.pydata.org/pandas-docs/stable/
Quick start: http://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html
Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.
Pandas is well suited for many different kinds of data:
-- Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
-- Ordered and unordered (not necessarily fixed-frequency) time series data.
-- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
-- Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure

Installing your own Python

Extending Python

Virtual environments

$ python -m venv my_environment

Running Python scripts on ARC